Data processing method and device

Abstract

A data processing method and device, which relates to the field of artificial intelligence technology, specifically in the fields of intelligent cloud, network communication, and large language models are provided. The data processing method is applied to a single-layer switch, where the single-layer switch is configured to complete a target operation, and the target operation includes multiple stage operations. The method includes: receiving multiple in-network computation requests sent by a current GPU, where the multiple in-network computation requests correspond to the multiple stage operations one by one; parallelly executing the multiple stage operations for multiple GPUs in a target group where the current GPU is located based on the multiple in-network computation requests.

What is claimed is: 
     
         1 . A data processing method, applied to a single-layer switch, wherein the single-layer switch is configured to complete a target operation, the target operation comprises multiple stage operations, and the method comprises:
 receiving multiple in-network computation requests sent by a current GPU, wherein the multiple in-network computation requests correspond to the multiple stage operations one by one;   parallelly executing the multiple stage operations for multiple GPUs in a target group where the current GPU is located, based on the multiple in-network computation requests.   
     
     
         2 . The method according to  claim 1 , wherein,
 the multiple in-network computation requests are received by a current port connected to the current GPU;   the parallelly executing the multiple stage operations for multiple GPUs in the target group where the current GPU is located based on the multiple in-network computation requests comprises:   at the current port, determining the multiple GPUs in the target group based on each in-network computation request; receiving data to be processed for each stage operation sent by each GPU of the multiple GPUs; obtaining result data of each stage operation based on the data to be processed; sending the result data of each stage to the current GPU;   wherein data to be processed of different stage operations are received in parallel, and/or result data of different stage operations are sent in parallel.   
     
     
         3 . The method according to  claim 2 , wherein,
 each in-network computation request comprises a current group identifier of the target group;   the determining the multiple GPUs in the target group based on each in-network computation request comprises:   determining the multiple GPUs as group members corresponding to the current group identifier based on a pre-established correspondence between group identifiers and group members.   
     
     
         4 . The method according to  claim 2 , wherein,
 the target operation is an AllReduce operation;   the AllReduce operation comprises: a ReduceScatter stage operation;   the data to be processed comprises: original data of each GPU;   the result data comprises: local reduced data of the current GPU;   the obtaining the result data of each stage operation based on the data to be processed comprises:   performing reduction processing based on the original data of each GPU to obtain local reduced data corresponding to the current GPU.   
     
     
         5 . The method according to  claim 4 , wherein the receiving the data to be processed for each stage operation sent by each GPU of the multiple GPUs comprises:
 sending a load request to each GPU, wherein the load request is configured to trigger each GPU to send original data;   receiving a load response sent by each GPU, wherein the load response comprises the original data of each GPU.   
     
     
         6 . The method according to  claim 2 , wherein,
 the target operation is an AllReduce operation;   the multiple stage operations comprise: an AllGather stage operation;   the data to be processed comprises: local reduced data of each GPU;   the current result data comprises: global reduced data;   the obtaining the result data of each stage operation based on the data to be processed comprises:   performing aggregation processing on the local reduced data of each GPU to obtain the global reduced data.   
     
     
         7 . The method according to  claim 6 , wherein the sending the global reduced data to the current GPU comprises:
 sending a storage request to the current GPU, wherein the storage request comprises the global reduced data.   
     
     
         8 . A data processing method, applied to a GPU, comprising:
 obtaining connection information of a single-layer switch, wherein the single-layer switch is configured to complete a target operation, and the target operation comprises multiple stage operations;   sending multiple in-network computation requests to the single-layer switch based on the connection information, wherein the multiple in-network computation requests correspond to the multiple stage operations one by one, so that the single-layer switch parallelly executes the multiple stage operations for multiple GPUs in a target group where the GPU is located based on the multiple in-network computation requests.   
     
     
         9 . An electronic device, comprising:
 at least one processor; and   a memory communicatively connected to the at least one processor; wherein,   the memory stores instructions executable by the at least one processor to cause the at least one processor to perform a data processing method, applied to a single-layer switch, wherein the single-layer switch is configured to complete a target operation, the target operation comprises multiple stage operations, and the method comprises:   receiving multiple in-network computation requests sent by a current GPU, wherein the multiple in-network computation requests correspond to the multiple stage operations one by one;   parallelly executing the multiple stage operations for multiple GPUs in a target group where the current GPU is located, based on the multiple in-network computation requests.   
     
     
         10 . The electronic device according to  claim 9 , wherein,
 the multiple in-network computation requests are received by a current port connected to the current GPU;   the parallelly executing the multiple stage operations for multiple GPUs in the target group where the current GPU is located based on the multiple in-network computation requests comprises:   at the current port, determining the multiple GPUs in the target group based on each in-network computation request; receiving data to be processed for each stage operation sent by each GPU of the multiple GPUs; obtaining result data of each stage operation based on the data to be processed; sending the result data of each stage to the current GPU;   wherein data to be processed of different stage operations are received in parallel, and/or result data of different stage operations are sent in parallel.   
     
     
         11 . The electronic device according to  claim 10 , wherein,
 each in-network computation request comprises a current group identifier of the target group;   the determining the multiple GPUs in the target group based on each in-network computation request comprises:   determining the multiple GPUs as group members corresponding to the current group identifier based on a pre-established correspondence between group identifiers and group members.   
     
     
         12 . The electronic device according to  claim 10 , wherein,
 the target operation is an AllReduce operation;   the AllReduce operation comprises: a ReduceScatter stage operation;   the data to be processed comprises: original data of each GPU;   the result data comprises: local reduced data of the current GPU;   the obtaining the result data of each stage operation based on the data to be processed comprises:   performing reduction processing based on the original data of each GPU to obtain local reduced data corresponding to the current GPU.   
     
     
         13 . The electronic device according to  claim 12 , wherein the receiving the data to be processed for each stage operation sent by each GPU of the multiple GPUs comprises:
 sending a load request to each GPU, wherein the load request is configured to trigger each GPU to send original data;   receiving a load response sent by each GPU, wherein the load response comprises the original data of each GPU.   
     
     
         14 . The electronic device according to  claim 10 , wherein,
 the target operation is an AllReduce operation;   the multiple stage operations comprise: an AllGather stage operation;   the data to be processed comprises: local reduced data of each GPU;   the current result data comprises: global reduced data;   the obtaining the result data of each stage operation based on the data to be processed comprises:   performing aggregation processing on the local reduced data of each GPU to obtain the global reduced data.   
     
     
         15 . The electronic device according to  claim 14 , wherein the sending the global reduced data to the current GPU comprises:
 sending a storage request to the current GPU, wherein the storage request comprises the global reduced data.